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An iterative algorithm learning the maximal margin classifier
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DOI：10.1016/S0031-3203(03)00060-8.png)
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Abstract
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A simple learning algorithm for maximal margin classifiers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger-Kozinec algorithm (S-K-algorithm) from 1981 which finds a maximal margin hyperplane with a given precision for separable data. We suggest a generalization of the S-K-algorithm (i) to the non-linear case using kernel functions and (ii) for non-separable data. The requirement in memory storage is linear to the data. This property allows the proposed algorithm to be used for large training problems. The resulting algorithm is simple to implement and as the experiments showed competitive to the state-of-the-art algorithms. The implementation of the algorithm in Matlab is available. We tested the algorithm on the problem aiming at recognition poor quality numerals. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
pattern recognition
linear classifier
supervised learning
support vector machines
kernel functions
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